A complete cookbook for building a self-hosted mail stack with single sign-on across webmail and desktop clients — no app passwords required.
Stack:
Статья опубликована под лицензией Creative Commons BY-NC-SA.
Три месяца назад здесь на Хабре я опубликовал статью "Интернет-цензура и обход блокировок: не время расслабляться", в которой простыми примерами показывалось, что практически все популярные у нашего населения для обхода блокировок VPN- и прокси-протоколы, такие как Wireguard, L2TP/IPSec, и даже SoftEther VPN, SSTP и туннель-через-SSH, могут быть довольно легко детектированы цензорами и заблокированы при должном желании. На фоне слухов о том, что Роскомнадзор активно обменивается опытом блокировок с коллегами из Китая и блокировках популярных VPN-сервисов, у многих людей стали возникать вопросы, что же делать и какие технологии использовать для получе
| # npsol.nim | |
| # Compile & run: | |
| # nim c -r -d:release -d:opencl -d:blas=openblas --mm:refc npsol.nim | |
| import std/[math, strformat, sequtils, os] | |
| import arraymancer | |
| import arraymancer/tensor/tensor_opencl | |
| import arraymancer/tensor/backend/opencl_backend | |
| import numericalnim | |
| import opencl | |
| import datamancer |
| #!/opt/homebrew/bin/bash | |
| # Thank you to [NerdyDeedsLLC](https://gist.github.com/NerdyDeedsLLC) for this | |
| # | |
| # LICENSED UNDER | |
| # https://gist.github.com/NerdyDeedsLLC/28b9157c36b195efd103a5e3aaf85eea | |
| set -euo pipefail | |
| SHELLDOT="$HOME/shelldot" |
| { | |
| "name": "Dental clinic speed to lead", | |
| "nodes": [ | |
| { | |
| "parameters": { | |
| "httpMethod": "POST", | |
| "path": "00000000-0000-4000-8000-000000000001", | |
| "options": {} | |
| }, | |
| "type": "n8n-nodes-base.webhook", |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| // from https://chromewebstore.google.com/detail/insensitivex/jeiefmcaoiljfljbclanpcmcfmfkdaen | |
| // the extension isn't open source, i didn't trust it. | |
| // it just runs this script on page load for the x.com domain. looks good. for now. | |
| // still, stuff happens: https://cybernews.com/security/chrome-extension-ownership-transfer-malware/ | |
| (function () { | |
| let value; | |
| Object.defineProperty(window, '__INITIAL_STATE__', { |
| name | extract-clothing-cutouts |
|---|---|
| description | Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory. |
Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.
